User Experience Metrics for Success

🔮 UX Metrics and KPIs Cheatsheet (Figma) (https://lnkd.in/en9MK4MD), a helpful reference sheet for UX metrics, with formulas and examples — for brand score, desirability, loyalty, satisfaction, sentiment, success, usefulness and many others. Neatly put together in one single place by fine folks at Helio Glare. To me personally, measuring UX success is focused around just a few key attributes — how successful users are in completing their key tasks, how many errors users experience along the way and how quickly users get through onboarding to first meaningful success. The context of the project will of course request specific, custom metrics — e.g. search quality score, or brand score, or engagement score or loyalty — but UX metrics are all around delivering value to users through their successes. Here are some examples: 1. Top tasks success > 80% (for critical tasks) 2. Time to complete top tasks < Xs (for critical tasks) 3. Time to first success < 90s (for onboarding) 4. Time to candidates < 120s (nav + filtering in eCommerce) 5. Time to top candidate < 120s (for feature comparison) 6. Time to hit the limit of a free tier < 7d (for upgrades) 7. Presets/templates usage > 80% per user (to boost efficiency) 8. Filters used per session > 5 per user (quality of filtering) 9. Feature adoption rate > 30% (usage of a new feature per user) 10. Feature retention rate > 40% (after 90 days) 11. Time to pricing quote < 2 weeks (for B2B systems) 12. Application processing time < 2 weeks (online banking) 13. Default settings correction < 10% (quality of defaults) 14. Relevance of top 100 search queries > 80% (for top 5 results) 15. Service desk inquiries < 35/week (poor design → more inquiries) 16. Form input accuracy ≈ 100% (user input in forms) 17. Frequency of errors < 3/visit (mistaps, double-clicks) 18. Password recovery frequency < 5% per user (for auth) 19. Fake email addresses < 5% (newsletters) 20. Helpdesk follow-up rate < 4% (quality of service desk replies) 21. “Turn-around” score < 1 week (frustrated users -> happy users) 22. Environmental impact < 0.3g/page request (sustainability) 23. Frustration score < 10% (AUS + SUS/SUPR-Q) 24. System Usability Scale > 75 (usability) 25. Accessible Usability Scale (AUS) > 75 (accessibility) Each team works with 3–4 design KPIs that reflect the impact of their work. Search team works with search quality score, onboarding team works with time to success, authentication team works with password recovery rate. What gets measured, gets better. And it gives you the data you need to monitor and visualize the impact of your design work. Once it becomes a second nature of your process, not only will you have an easier time for getting buy-in, but also build enough trust to boost UX in a company with low UX maturity. [continues in comments ↓] #ux #design

Data is everything in product design. Without data, we open ourselves up to: - Biases - Opinions - Confusion - Misalignment When we are data-informed and that data is accurate, we can truly make educated product decisions. I like to think of data in two layers: a) What’s happening and b) Why it’s happening. Let’s break it down. What’s happening: - Business data tells us how the business is doing - Marketing/sales data tells us where our customers come from - Retention data tells us when and why customers are leaving us - Engagement data tells us how customers are using our product Why it’s happening: - User research gives us rich insight into why something is happening - Voice of the customer data shows us how customers talk about our product - Usability scores show us how people perceive our product or feature experience in a measurable way - Product market fit & satisfaction scores give us a simple and actionable metric to track and improve over time In terms of accessing that data, methodologies vary, but generally speaking, I always advise the following: 1. Get access to growth and retention data through business dashboards. 2. Get access to product data through your product analytics tool. 3. Set up a cadence to gather customer reviews & comments, either manually or via automated tools. 4. Set up a cadence to speak to your users continuously to answer the why. 5. Set up a recurring survey to track satisfaction and usability. If you don’t have the data structure for any of the above, speak to your product and data team to see if you can change that. If not, rely on the data that you can actually get. PS: The list of metrics is indicative: Actual metrics will differ greatly from one company to another and largely depend on the industry, niche, as well as your data infrastructure and setup. — If you found this useful, consider reposting ♻️ How are you collecting and using data in your design process? What else are you tracking?

🔎 UX Metrics: How to Measure and Optimize User Experience? When we talk about UX, we know that good decisions must be data-driven. But how can we measure something as subjective as user experience? 🤔 Here are some of the key UX metrics that help turn perceptions into actionable insights: 📌 Experience Metrics: Evaluate user satisfaction and perception. Examples: ✅ NPS (Net Promoter Score) – Measures user loyalty to the brand. ✅ CSAT (Customer Satisfaction Score) – Captures user satisfaction at key moments. ✅ CES (Customer Effort Score) – Assesses the effort needed to complete an action. 📌 Behavioral Metrics: Analyze how users interact with the product. Examples: 📊 Conversion Rate – How many users complete the desired action? 📊 Drop-off Rate – At what stage do users give up? 📊 Average Task Time – How long does it take to complete an action? 📌 Adoption and Retention Metrics: Show engagement over time. Examples: 📈 Active Users – How many people use the product regularly? 📈 Churn Rate – How many users stop using the service? 📈 Cohort Retention – What percentage of users remain engaged after a certain period? UX metrics are more than just numbers – they tell the story of how users experience a product. With them, we can identify problems, test hypotheses, and create better experiences! 💡🚀 📢 What UX metrics do you use in your daily work? Let’s exchange ideas in the comments! 👇 #UX #UserExperience #UXMetrics #Design #Research #Product

Here's 7 key metrics every AI PM should track — not just to measure engagement, but to ensure your AI is useful, safe, and trusted. Too often, we focus on DAU or churn… but, especially if you're building conversational products, you need new metrics — ones that capture meaning, depth, and trust. Here’s the framework I use 👇 I used a pyramid because each layer supports the next: without factual, safe foundations, you can’t earn trust or scale responsibly. -The foundation is Model Quality — your AI must be accurate, safe, and fast before anything else matters. -Above that is Interaction Quality — can users have meaningful, multi-turn conversations that feel natural and helpful? -Then comes Trust & Delight — do users enjoy the experience and come back because they trust it? -Higher still is User Value — are people actually achieving their goals faster, easier, and better? - And at the top sits Sustainability — are you doing all of this responsibly and efficiently (revenue / compute $, LTV / CAC)? Success in conversational AI = Useful × Safe × Trusted <><><><><><><><><><> Follow Marily Nika, Ph.D for AI PM education, certifications and insights.

🔥 If your Data Catalog isn’t measured, it’s probably failing. Most data catalogs don’t fail because of technology. They fail because success is never clearly defined. So let’s be blunt. Here’s how you actually know whether your data catalog works. ❌ Vanity metric to forget: “Number of datasets cataloged” ✔️ Metrics that matter: 🔴 1. Do people come back? (Adoption) One login ≠ success. Are users still active after onboarding? Are they searching… or asking Slack instead? If usage drops, your catalog is just expensive documentation. 🔴 2. Is the metadata good enough to trust? Auto-ingested metadata ≠ usable metadata. Do datasets have owners? Are descriptions written for humans? No context = no trust = no usage. 🔴 3. Does it actually save time? If analysts still spend hours “data hunting”, the catalog failed. Can users find the right dataset in minutes? Are the same questions still asked every week? If nothing changes, value is zero. 🔴 4. Who is accountable for the data? “Shared responsibility” usually means “no responsibility”. Is every critical dataset owned? Do stewards respond? Governance starts with naming names. 🔴 5. Can users tell which data is safe to use? Without trust signals, catalogs create confusion — not clarity. Certified datasets Data quality visibility Clear warnings for risky data No signals = no confidence = shadow data. 🔴 6. Is the platform reducing manual effort — or creating more? If stewardship feels like extra work, it won’t scale. How much is automated? Is steward workload increasing or decreasing? If governance doesn’t scale, it dies. 🔴 7. Does the business feel the impact? This is the uncomfortable question. Faster decisions? More reuse? Fewer duplicated datasets? If leadership can’t feel the difference, they won’t fund it. ⚠️ Hard truth: A data catalog is not a compliance tool. It’s not a metadata repository. It’s not a checkbox. It’s a product, and products live or die by adoption, trust, and impact. 💬 Be honest: Which of these KPIs are you actually tracking today?

Everyone’s excited to launch AI agents. Almost no one knows how to measure if they’re actually working. Over the last year, we’ve seen brands launch everything from GenAI assistants to support bots to creative copilots but the post-launch metrics often look like this: • Number of chats • Average latency • Session duration • Daily active users Useful? Yes. But sufficient? Not even close. At ALTRD, we’ve worked on AI agents for enterprises and if there’s one lesson it’s this: Speed and usage mean nothing if the agent isn’t solving the actual problem. The real performance indicators are far more nuanced. Here’s what we’ve learned to track instead: 🔹 Task Completion Rate — Can the AI go beyond answering a question and actually complete a workflow? 🔹 User Trust — Do people come back? Do they feel confident relying on the agent again? 🔹 Conversation Depth — Is the agent handling complex, multi-turn exchanges with consistency? 🔹 Context Retention — Can it remember prior interactions and respond accordingly? 🔹 Cost per Successful Interaction — Not just cost per query, but cost per outcome. Massive difference. One of our clients initially celebrated their bot’s 1 million+ sessions - until we uncovered that less than 8% of users actually got what they came for. That 8% wasn’t a usage issue. It was a design and evaluation issue. They had optimized for traffic. Not trust. Not success. Not satisfaction. So we rebuilt the evaluation framework - adding feedback loops, success markers, and goal-completion metrics. The results? CSAT up by 34% Drop-off down by 40% Same infra cost, 3x more value delivered The takeaway: Don’t just measure what’s easy. Measure what matters. AI agents aren’t just tools - they’re touchpoints. They represent your brand, shape user experience, and influence business outcomes. P.S. What’s one underrated metric you’ve used to evaluate AI performance? Curious to learn what others are tracking.

“What’s important to your customers has to show up in the numbers that are important to your business.” - Me Not sure how? I got you today. Keep reading for three practical ways to do it. 1. Time to Value: If your customers care about speed, then your business metrics should too. Stop measuring “average handle time” in a vacuum and start tracking “time to value” from the customer’s first touch to when they feel the outcome. If customers get value faster, you’ll see it show up in renewals and wallet share. Your Chief Revenue Officer will be so happy he’s flying you and your spouse to Nantucket to stay at their summer house in Sconset. Question to answer: if you cut your customers’ “time to value” in half, what would it do to your revenue line? 2. Cost to Serve: What’s important to customers isn’t just a great customer service interaction. It’s seamless service. Every time your customers hit unnecessary friction, you’re forcing more calls, chats, and escalations. That means higher cost to serve for you. Track “first contact resolution” or “friction-free journeys.” When customers solve issues on the first try, whether digital or via an agent, trust goes up and operating costs come down. Your COO will be so happy that they’re advocating for you to receive more RSUs this year. Question to answer: how much wasted spend is hidden in repeat contacts your team is fielding today? 3. Risk Reduction: Customers want trust and reliability. That’s not soft stuff. It’s hard risk. A single failure in onboarding, data accuracy, or billing erodes confidence and creates churn risk. Tie customer trust to metrics like churn reduction or compliance adherence. When customers feel secure, risk of lost revenue or regulatory risk gets so low your Chief Risk Officer is sending you cookies over the winter holidays. Be honest: are your trust metrics showing up in your quarterly business review deck, or are they buried in a VOC report no one reads? #clientexperience #leadership #growth #efficiency #coo #cro #voc

📈 Improve your case studies with UX metrics. If you've been avoiding metrics in your UX portfolio, it's time to change it! In a competitive job market, setting yourself apart means proving your efforts make a real impact on UX projects. This is also something recruiters and managers truly value. They want to see numbers and evidence, not just beautiful designs. Here are some common UX metrics to showcase in your projects: ✅ Task success ⏩ Example: Task success rate was increased by X% percent. Measure this during usability testing or by reviewing analytics tracking tools. ✅ User satisfaction ⏩ Example: User satisfaction rate improved by X points. Gather data through user surveys, star ratings, or other user feedback forms. ✅ Time spent on task ⏩ Example: The average time spent on task was decreased by X% After design changes measure time spent on tasks and compare it with the old design. ✅ Conversion rate ⏩ Example: Sign up rate increased by X% This is a powerful metrics that impacts business goals and is often applied to app/website sign ups, lead collection forms, etc. ✅ Feature adoption ⏩ Example: X% of users started using this new feature within a month. Track this with analytics tools to see how many users adopt the new feature and analyze whether it brings value to them. ✅ Error rate ⏩ Example: For the given task the error rate was decreased by X% To calculate the error rate, count the number of errors users make during completing task and compare it with old error rate. Which UX metrics do you use in your projects? Share your experiences. -------- Hi, I’m Tetiana Gulei I help you break into the UX design industry and grow as a designer. 🔔 Follow me for more UX insights and UX career tips. ✉️ Want me to review your portfolio? Send me a DM. #uxportfolio #uxdesign #uxtips

💡Measuring UX using Google HEART HEART is a framework developed by Google for evaluating the user experience of a product. It provides a holistic view of the UX by considering both qualitative & quantitative metrics. HEART stands for ✅ Happiness: How satisfied users are with using your product. It can be measured through surveys and ratings (quantitative) and reviews and user interviews (qualitative). Tracking happiness is right when you analyze the general performance of your product. ✅ Engagement: How actively users are interacting with the product. This includes metrics like the number of visits, time spent on the product, frequency of interactions, and the depth of interactions (e.g., the number of features used). Analyzing engagement will help you understand how compelling & valuable the product is to users. ✅ Adoption: How effectively the product attracts new users and converts them into active users. Key metrics include user sign-ups, onboarding completion rates, and activation rates (e.g., the percentage of users who perform a key action after signing up). Understanding adoption helps identify barriers during product onboarding. ✅ Retention: How well the product retains its users over time. It focuses on reducing churn and keeping users engaged over the long term. Metrics like retention rate and cohort analysis are used to measure retention. Improving retention involves addressing pain points, providing ongoing value, and fostering a sense of loyalty among users. ✅ Task success: How effectively users can accomplish their goals or tasks using the product. This includes metrics like task completion rate, error rate, and time to complete tasks. User journey mapping, user interviews, and usability testing can help identify usability issues and optimize the user flow to enhance task success. ❗ Top 3 mistakes when using HEART 1️⃣ Placing too much emphasis on quantitative metrics at the expense of qualitative insights. While quantitative data is valuable for analysis, it's essential to complement this with qualitative data, such as user feedback and observations, to gain a deeper understanding of user behavior and preferences. 2️⃣ Ignoring the context of interaction: Failing to consider the context in which users interact with the product can lead to misleading interpretations of the data.  3️⃣ Lack of user segmentation: Not segmenting users based on relevant factors such as demographics, behavior, or usage patterns can obscure important insights and lead to generic conclusions that may not apply to all user groups. 📺 Guide to using Google HEART: https://lnkd.in/dhkwy_jN 🚨 Live session "How to measure design success" 🚨 I will run a live session on measuring design success in February. Will talk about how to choose the right metrics for your product & how to measure product's success in meeting business goals   https://lnkd.in/dgm6t_jf #UX #design #productdesign #metrics #measure

Ever looked at a UX survey and thought: “Okay… but what’s really going on here?” Same. I’ve been digging into how factor analysis can turn messy survey responses into meaningful insights. Not just to clean up the data - but to actually uncover the deeper psychological patterns underneath the numbers. Instead of just asking “Is this usable?”, we can ask: What makes it feel usable? Which moments in the experience build trust? Are we measuring the same idea in slightly different ways? These are the kinds of questions that factor analysis helps answer - by identifying latent constructs like satisfaction, ease, or emotional clarity that sit beneath the surface of our metrics. You don’t need hundreds of responses or a big-budget team to get started. With the right methods, even small UX teams can design sharper surveys and uncover deeper insights. EFA (exploratory factor analysis) helps uncover patterns you didn’t know to look for - great for new or evolving research. CFA (confirmatory factor analysis) lets you test whether your idea of a UX concept (say, trust or usability) holds up in the real data. And SEM (structural equation modeling) maps how those factors connect - like how ease of use builds trust, which in turn drives satisfaction and intent to return. What makes this even more accessible now are modern techniques like Bayesian CFA (ideal when you’re working with small datasets or want to include expert assumptions), non-linear modeling (to better capture how people actually behave), and robust estimation (to keep results stable even when the data’s messy or skewed). These methods aren’t just for academics - they’re practical, powerful tools that help UX teams design better experiences, grounded in real data.